Satellite　remote　sensing　can　detect　and　predict　subsurface　temperature　and　salinity　structure　within　the　ocean　over　large　scales.　In　the　era　of　big　ocean　data,　making　full　use　of　multisource　satellite　observations　to　accurately　detect　and　predict　global　subsurface　thermohaline　structure　and　advance　our　understanding　of　the　ocean　interior　processes　is　extremely　challenging.　This　study　proposed　a　new　deep　learning-based　method-bi-directional　long　short-term　memory　(Bi-LSTM)　neural　networks-for　predicting　global　ocean　subsurface　temperature　and　salinity　anomalies　in　combination　with　surface　remote　sensing　observations　(sea-surface　temperature　anomaly,　sea-surface　height　anomaly,　sea-surface　salinity　anomaly,　and　the　northward　and　eastward　components　of　seasurface　wind　anomaly),　longitude　and　latitude　information　(LON　and　LAT),　and　subsurface　Argo　gridded　data.　Because　of　the　temporal　dependency　and　periodicity　of　ocean　dynamic　parameters,　the　Bi-LSTM　is　good　at　time-series　feature　learning　by　considering　the　significant　temporal　feature　of　the　ocean　variability　and　can　well　improve　the　robustness　and　generalization　ability　of　the　prediction　model.　For　December　2015　as　an　example,　our　average　prediction　results　in　an　overall　determination　coefficient　(R-2)　of　0.691/0.392　and　a　normalized　root　mean　square　error　of　0.039　degrees　C/0.051　PSU　for　subsurface　temperature　anomaly　(STA)/subsurface　salinity　anomaly　(SSA)　prediction.　This　study　sets　up　different　cases　based　on　different　sea-surface　feature　combinations　to　predict　the　subsurface　thermohaline　structure　and　analyze　the　role　of　longitude　and　latitude　information　on　Bi-LSTM　prediction.　The　results　show　that　in　the　prediction　of　STA,　the　contribution　of　LON　+　LAT　to　the　model　gradually　increases　with　depth,　whereas　in　the　prediction　of　SSA,　LON　+　LAT　maintains　a　relatively　significant　contribution　to　the　model　at　different　depths.　Meanwhile,　in　the　STA　and　SSA　prediction,　the　LAT　plays　a　more　significant　role　than　LON.　We　also　applied　the　model　to　bi-directional　prediction　for　different　months　of　2010　and　2015　to　prove　the　applicability　and　robustness　of　the　model.　This　study　suggests　that　Bi-LSTM　is　more　advantageous　in　time-series　modeling　for　predicting　subsurface　and　deep　ocean　temperature　and　salinity　structures,　fully　takes　into　account　the　timing　dependence　of　global　ocean　data,　and　outperforms　the　classic　random　forest　approach　in　predicting　subsurface　thermohaline　structure　from　remote　sensing　data.